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Merge pull request #399 from Living-with-machines/april_workshop
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April workshop
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@rwood-97
rwood-97 authored Apr 30, 2024
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361 changes: 361 additions & 0 deletions worked_examples/geospatial/workshop_april_2024/Workshop_AprilMay2024_DeepSolo.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mapreader Workshop - 30th April - 1st May 2024"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First check you have the correct version of MapReader. \n",
"For the April 2024 workshop, this is v1.3.0\n",
"\n",
"This can be downloaded from pypi using `pip install mapreader==1.3.0` or by checking out the repo at [this commit](https://github.com/Living-with-machines/MapReader/releases/tag/v1.3.0)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mapreader\n",
"assert mapreader.__version__ == '1.3.0'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will start by creating a new ``MapImages`` object by loading our 1000 pixel patches and adding our ``metadata.csv``.\n",
"\n",
"To speed things up for the workshop, we will just load patches corresponding to ``map_75650907.png``."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from mapreader import load_patches"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_files = load_patches(patch_paths=\"./patches_1000_pixel/*75650907*png\", parent_paths=\"./maps/map_75650907.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_files.add_metadata(\"./maps/metadata.csv\", ignore_mismatch=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then need to run the ``add_coord_increments()`` method, which calculates the change in latitude (dlat) and longitude (dlon) across each pixel.\n",
"This will be used to convert pixel coordinates to geo coordinates."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_files.add_coord_increments()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"parent_df, patch_df = my_files.convert_images()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"parent_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"patch_df.head()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Detect and recognise text\n",
"\n",
"Now we can load an already trained/fine-tuned text detection model and run the model inference on our patches. \n",
"\n",
"DeepSolo is a text detection and recognition framework so it produces bounding boxes, recognised text outputs and scores."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set up the DeepSoloRunner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from mapreader import DeepSoloRunner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# change these to your own paths, see the docs for more details on how to get these\n",
"cfg_file = \"/Users/rwood/projects/DataCulture/ocr_test/detectron2_etc/DeepSolo/configs/R_50/IC15/finetune_150k_tt_mlt_13_15_textocr.yaml\"\n",
"weights_file = \"/Users/rwood/projects/DataCulture/ocr_test/detectron2_etc/ic15_res50_finetune_synth-tt-mlt-13-15-textocr.pth\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"my_runner = DeepSoloRunner(\n",
" patch_df,\n",
" parent_df,\n",
" cfg_file = cfg_file,\n",
" weights_file = weights_file,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run on all patches in the patch dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"patch_predictions = my_runner.run_all(return_dataframe=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"patch_predictions.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can visualise the outputs for each patch with the show method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_runner.show(\n",
" \"patch-4000-2000-5000-3000-#map_75650907.png#.png\",\n",
" figsize=(15, 15),\n",
" border_color=\"r\",\n",
" text_color=\"b\",\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scale up to parent images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``convert_to_parent_pixel_bounds()`` method takes all the patch predictions and scales the up to the parent image.\n",
"This means all our bounding boxes are grouped at the parent-level, rather than at the patch-level."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"parent_predictions = my_runner.convert_to_parent_pixel_bounds(return_dataframe=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"parent_predictions.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can visualise the outputs for each parent with the show method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_runner.show(\n",
" \"map_75650907.png\", \n",
" figsize=(15, 15),\n",
" border_color=\"r\",\n",
" text_color=\"b\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convert pixel bounds to coordinates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we added our dlat and dlon (coordinate increments), we can convert out pixel coordinates to geo coordinates.\n",
"This will mean our bounding boxes are georeferenced."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geo_predictions = my_runner.convert_to_coords(return_dataframe=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Saving these outputs will give you a geojson file you can load into a GIS software."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_runner.save_to_geojson(\"./deepsolo_outputs.geojson\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "mr_dev",
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}
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